Learning Causal Dynamics Models in Object-Oriented Environments

This is the source code for the paper "Learning Causal Dynamics Models in Object-Oriented Environments", which was accpeted in ICML 2024.

By Zhongwei Yu.

Installation

First, you should have installed python >= 3.9 in your device. Then, make sure you have installed the python dependencies in requirements.txt.

If you want to perform experiments in StarCraftII environments (CMS and DZB), please download and install StarCraftII in your device, and set the environment variable SC2PATH correctly.

Usage

Configure experiments

We use the class Config to contain hyper-parameters and basic configurations of experiments (i.e., number of samples, number of iterations, the device, etc). You can specify these hyper-parameters in configs.py as you want (check config_mouse, config_block, CONFIG_CMS and CONFIG_DZB).

You can specify which experiment to perform by modifying run.py:

• experiment_1 trains dynamics models in a task. Then it measures the log-likelihoods, and evaluate the average episodic returns of planning. You can select the environments and approaches by modifying annotations.
• experiment_2 trains OO models in the seen tasks of Mouse environment. Then it measures log-likelihoods and episodic returns in both seen and unseen tasks.

Run experiments

To run experiments, simply use the following command:

python run.py --seed=[SEED]

Check your results

All results, arguments, and logs of experiments will be stored in the experiments directory. If it does not exists, it will be automatically created when you run any experiment. The results will be saved at the

./experiments/[ENVIRONMENT]/train[APPROACH]/seed-[SEED]

We provide a tool in show_results.py to conveniently examine the results of different seeds. In the following, we show an example of how to use this tool.

> python show_results.py
The root directory has not been set yet. Use 'root' command to set the root.
This program shows results saved in experiments.
show-result > root experiments/cms/trainooc
Found results.json in the following runs:
- seed-5
- seed-2
- seed-4
- seed-6
- seed-9
- seed-8
- seed-3
- seed-1
- seed-7
- seed-10
show-result > set_filter seed-(6|7|8|9|10)
Found results.json in the following runs:
- seed-6
- seed-9
- seed-8
- seed-7
- seed-10
show-result > show test/loglikelihood
seed-6: 8.987736654281616
seed-9: 8.26842833518982
seed-8: 8.839284439086914
seed-7: 9.881512908935546
seed-10: 8.59796847820282
show-result > stat test/return
mean: 3.4239471440750444
max: 9.895652173913073
min: -5.208333333333319
std: 6.275761134625612
median 5.711764705882381